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Datasets:

pianistprogrammer
/
Meter2800 

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Tasks:
Audio Classification
Modalities:
Audio
Languages:
English
Tags:
audio
music-classification
meter-classification
multi-class-classification
multi-label-classification
License:

mit
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Meter2800
/
meter2800.py

pianistprogrammer's picture
pianistprogrammer
Refactor Meter2800 dataset configuration and example generation logic
ec015f3
1 minute ago
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from pathlib import Path
import datasets
import pandas as pd

_CITATION = """\
@misc{meter2800_dataset,
  author       = {PianistProgrammer},
  title        = {{Meter2800}: A Dataset for Music Time signature detection / Meter Classification},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/pianistprogrammer/Meter2800}
}
"""

_DESCRIPTION = """\
Meter2800 is a dataset of 2,800 music audio samples for automatic meter classification.
Each audio file is annotated with a primary meter class label (e.g., 'two', 'three', 'four') 
and an alternative meter (numerical, e.g., 2, 3, 4, 6). 
It is split into training, validation, and test sets, each available in two class configurations:
2-class and 4-class. All audio is 16-bit WAV format.
"""

_HOMEPAGE = "https://huggingface.co/datasets/pianistprogrammer/Meter2800"
_LICENSE = "mit"

# Define the labels - adjust these based on your actual data
LABELS_4 = ["three", "four", "five", "seven"]
LABELS_2 = ["simple", "complex"]  # or whatever your 2-class grouping actually is

class Meter2800Config(datasets.BuilderConfig):
    """BuilderConfig for Meter2800."""
    def __init__(self, name, **kwargs):
        super(Meter2800Config, self).__init__(
            name=name,
            version=datasets.Version("1.0.0"),
            **kwargs
        )

class Meter2800(datasets.GeneratorBasedBuilder):
    """Meter2800 dataset."""
    
    BUILDER_CONFIGS = [
        Meter2800Config(
            name="4_classes",
            description="4-class meter classification"
        ),
        Meter2800Config(
            name="2_classes", 
            description="2-class meter classification"
        ),
    ]
    
    DEFAULT_CONFIG_NAME = "4_classes"

    def _info(self):
        if self.config.name == "4_classes":
            label_names = LABELS_4
        elif self.config.name == "2_classes":
            label_names = LABELS_2
        else:
            # Fallback - shouldn't happen with proper configs
            label_names = LABELS_4
            
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "filename": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=None),
                "label": datasets.ClassLabel(names=label_names),
                "meter": datasets.Value("string"),
                "alt_meter": datasets.Value("string"),
            }),
            supervised_keys=("audio", "label"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # Get the data directory
        data_dir = dl_manager.download_and_extract("")
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "csv_file": f"{data_dir}/data_train_{self.config.name}.csv",
                    "data_dir": data_dir
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "csv_file": f"{data_dir}/data_val_{self.config.name}.csv",
                    "data_dir": data_dir
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "csv_file": f"{data_dir}/data_test_{self.config.name}.csv",
                    "data_dir": data_dir
                },
            ),
        ]

    def _generate_examples(self, csv_file, data_dir):
        df = pd.read_csv(csv_file)
        df = df.dropna(subset=["filename", "label", "meter"]).reset_index(drop=True)

        for idx, row in df.iterrows():
            # Construct the full audio path
            audio_path = f"{data_dir}/{row['filename']}"
            
            yield idx, {
                "filename": row["filename"],
                "audio": audio_path,
                "label": row["label"],
                "meter": str(row["meter"]),
                "alt_meter": str(row.get("alt_meter", row["meter"])),
            }